Hi I'm looking to crowd-source some ideas here from people who perhaps have managed to do this. Taking guidance from http://jevois.org/tutorials/UserTensorFlowTraining.html on transfer learning on a custom dataset, I'm looking to extend the recognition to include object detection as well. I've chosen the baseline framework with SDD-MobileNet v2 and hopefully follow the steps using TensorFlow Object Detection API with a baseline model (ssdlite_mobilenet_v2_coco) to do transfer learning followed by inference optimization and conversion to TFlite to run on Jevois Cam.

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You would actually run a different module on JeVois, as TensorFlowEasy and that flowers tutorial is for object recognition only, but you want detection plus recognition. So you need a module that will get bounding boxes and then labels for the boxes. We have two types: YOLO and OpenCV DNN. Since your custom model is not derived from YOLO, you would use this

Then the steps to copy the model to JeVois are similar: after you have your trained model, you need to copy 3 files to your microSD into /jevois/share/opencv-dnn/ (for class names, model (.pb file), and model structure (.pbtxt file)), and then create the appropriate entries in the module's params.cfg. See the current contents of params.cfg towards the bottom of the page for DetectionDNN. You would end up with some new entries in params.cfg:

You could train your own model on Yolo too. See the write ups here: https://pjreddie.com/darknet/yolo/ and the readme here: https://github.com/AlexeyAB/darknet
Jevois uses a different implementation of YOLO than either of these - it uses NNPACK Darknet to take advantage of the CPU optimization. No matter, if you train it using Alexy's fork, the weights will work on Jevois. You'll want a GPU on the box you use for training, otherwise it's dreadfully slow.

Hi Peter, thanks for the recommendation. Just to check does Jevois support implementation of Yolo2-light? its the v2 implementation with quantization with 30% speedup and -1% mAP performance. Its using the BIT1-XNOR-inference framework though.